External memory graph traversal
External memory graph traversal is like playing a treasure hunt game where you need to find a path in a big maze.
Imagine you have a huge maze with lots of rooms and corridors, but you can only see a limited number of them at a time. So, you need to explore one part of the maze before moving on to the next.
In the same way, when we have a large graph (a network of points connected by lines), we can't store all the data in computer memory at once. It's like trying to fit a giant jigsaw puzzle into a small bag.
So, we divide the graph into smaller parts and save them into "external memory" (usually a hard disk or Solid-State Drive). Then we load one small part at a time into the computer memory and explore it.
To explore the graph, we need to find a starting point, and from there, we follow the lines to find other points. We can do this in different ways, such as depth-first or breadth-first search. It's like walking through the maze, looking for clues to find the treasure.
External memory graph traversal is mostly used when the graph is too large to fit into the memory or when we don't have enough memory to store the entire graph. With this technique, we can efficiently process and analyze large graphs.
Imagine you have a huge maze with lots of rooms and corridors, but you can only see a limited number of them at a time. So, you need to explore one part of the maze before moving on to the next.
In the same way, when we have a large graph (a network of points connected by lines), we can't store all the data in computer memory at once. It's like trying to fit a giant jigsaw puzzle into a small bag.
So, we divide the graph into smaller parts and save them into "external memory" (usually a hard disk or Solid-State Drive). Then we load one small part at a time into the computer memory and explore it.
To explore the graph, we need to find a starting point, and from there, we follow the lines to find other points. We can do this in different ways, such as depth-first or breadth-first search. It's like walking through the maze, looking for clues to find the treasure.
External memory graph traversal is mostly used when the graph is too large to fit into the memory or when we don't have enough memory to store the entire graph. With this technique, we can efficiently process and analyze large graphs.